Head-driven statistical models for natural language parsing
Statistical models for parsing natural language have recently shown considerable success in broad-coverage domains. Ambiguity often leads to an input sentence having many possible parse trees; statistical approaches assign a probability to each tree, thereby ranking competing trees in order of plausibility. The probability for each candidate tree is calculated as a product of terms, each term corresponding to some sub-structure within the tree. The choice of parameterization is the choice of how to break down the tree. There are two critical questions regarding the parameterization of the problem: (1) What linguistic objects (e.g., context-free rules, parse moves) should the model's parameters be associated with? I.e., How should trees be decomposed into smaller fragments? (2) How can this choice be instantiated in a sound probabilistic model? This thesis argues that the locality of a lexical head's influence in a tree should motivate modeling choices in the parsing problem. In the final parsing models a parse tree is represented as the sequence of decisions corresponding to a head-centered, top-down derivation of the tree. Independence assumptions then follow naturally, leading to parameters that encode the X-bar schema, subcategorization, ordering of complements, placement of adjuncts, lexical dependencies, wh-movement, and preferences for close attachment. All of these preferences are expressed by probabilities conditioned on lexical heads. The goals of the work are two-fold. First, we aim to advance the state of the art. We report tests on Wall Street Journal text showing that the models give improved accuracy over other methods in the literature. The models recover richer representations than previous approaches, adding the complement/adjunct distinction and information regarding wh-movement. Second, we aim to increase understanding of statistical parsing models. Each parameter type is motivated through tree examples where it provides discriminative information. An empirical study of prepositional phrase attachment ambiguity is used to investigate the effectiveness of dependency parameters for ambiguity resolution. A number of parsing models are tested, and we give a breakdown of their performance on different types of construction. Finally, we give a detailed comparison of the models to others in the literature.
Collins, Michael John, "Head-driven statistical models for natural language parsing" (1999). Dissertations available from ProQuest. AAI9926110.